Image enhancement techniques are essential for improving image quality. Sharp images delivered by effective enhancement techniques offer incomparable visual appeal to a viewer.
Traditional enhancement techniques enhance image detail by way of mask filters or other high-pass filters. Though such filters do enhance details, they also introduce many artifacts such as overshooting, halo effect, and ringing, and serve to make the processed image appear unnatural to the viewer.
Current edge-preserving enhancement methods may avoid overshooting, halo effect, and ringing artifacts but in so doing sacrifice the sharpening of edges. One technique includes progressively coarsening an image by way of an edge-preserving operator based on the weighted least squares (WLS) framework, constructing an edge-preserving multi-scale image decomposition, and then enhancing the details at multi-scales. This algorithm only enhances details that are not at an edge, however, and thus does not permit many image enhancement tasks. Moreover, the computation cost and time of WLS operations are significant because a WLS scheme needs a global optimization.
Other current techniques may achieve edge-aware detail enhancement through simple point-wise manipulation of Laplacian pyramids but, similar to the technique discussed above, this technique only enhances details that are not at an edge. Also, because for each pixel in every scale pixel remapping and Laplacian pyramids are computed in a region surrounding this pixel, the processing time and cost are both significant.
Other current techniques may enhance an image by way of a multi-scale morphology. Such techniques can achieve edge-sharpening without the above artifacts by increasing the value of a pixel (i.e., making the pixel brighter) if the pixel value is much closer to a local maximal pixel value than a local minimal one; otherwise, the pixel value is decreased, i.e., the pixel is made darker. Although this enhancement performance is generally better than other methods, the computational time and required resources are both high. Moreover, this technique is very sensitive to noise, especially in large-scale operations.